With continuous development of Internet technologies, a growing number of users may use the Internet for their activities including, for example, browsing news and online shopping. However, with continuous increase of information on the Internet, users may have to handle too much information content from the Internet. In order to help users with their interested information content, some Internet applications use interest tags to recommend information content to a user according to interest tags of the user.
An interest tag may include term(s)/word(s) for a user to describe the user's interest(s). For example, a user may use terms such as “basketball”, “NBA”, and “Jeremy Lin” as interest tags to describe own interests. Existing interest tag recommending methods include random recommendation and hot point recommendation. The random recommendation refers to recommending several interest tags to a user randomly, while the hot point recommendation refers to recommending interest tags by category to a user according to current hot events.
However, these existing recommendation methods have obvious disadvantages. The random recommendation method is lack of accuracy and has undesirable recommendation effect, and may recommend too many uninterested tags to a user. The hot point recommendation method can only include those popular interest tags and cannot make recommendations corresponding to user's selection and preference.
Therefore, there is a need to solve technical problems in the Internet and computer technology to improve accuracy for recommending interest tags to users.